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* continue to use paddleocr v3 text detection model for large v5 was not finding text on multi-line plates at all in testing * implement clustering of plate variants per event should reduce OCR inconsistencies and improve plate recognition stability by using string similarity to cluster similar variants (10 per event id) and choosing the highest confidence representative as the final plate * pass camera * prune number of variants based on detect fps * implement replacement rules for cleaning up and normalizing plates * docs * docs |
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.. | ||
camera | ||
__init__.py | ||
auth.py | ||
base.py | ||
camera_group.py | ||
classification.py | ||
config.py | ||
database.py | ||
env.py | ||
logger.py | ||
mqtt.py | ||
network.py | ||
proxy.py | ||
telemetry.py | ||
tls.py | ||
ui.py |